lpost_pow

Unnormalised log-posterior density of discrete power law

internal

The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <doi:10.48550/arXiv.2008.03073>, of which functions are provided.

Clement Lee

crandep

Network Analysis of Dependencies of CRAN Packages

lpost_pow function

<dl><dt>alpha</dt>
<dd>Real number greater than 1</dd>
<dt>df</dt>
<dd>A data frame with at least two columns, x &amp; count</dd>
<dt>m_alpha</dt>
<dd>Real number, mean of the prior normal distribution for alpha</dd>
<dt>s_alpha</dt>
<dd>Positive real number, standard deviation of the prior normal distribution for alpha</dd></dl>

Arguments

Unnormalised log-posterior density of discrete power law — lpost_pow

<dl>

<dt>alpha</dt>
<dd>Real number greater than 1</dd>


<dt>df</dt>
<dd>A data frame with at least two columns, x &amp; count</dd>


<dt>m_alpha</dt>
<dd>Real number, mean of the prior normal distribution for alpha</dd>


<dt>s_alpha</dt>
<dd>Positive real number, standard deviation of the prior normal distribution for alpha</dd>

</dl>

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lpost_pow: Unnormalised log-posterior density of discrete power law

Description

Usage

Value

Arguments